Pharmacophore-driven identification of PPARγ agonists from natural sources

In a search for more effective and safe anti-diabetic compounds, we developed a pharmacophore model based on partial agonists of PPARγ. The model was used for the virtual screening of the Chinese Natural Product Database (CNPD), a library of plant-derived natural products primarily used in folk medi...

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Bibliographic Details
Published in:Journal of computer-aided molecular design 2011-02, Vol.25 (2), p.107-116
Main Authors: Petersen, Rasmus K., Christensen, Kathrine B., Assimopoulou, Andreana N., Fretté, Xavier, Papageorgiou, Vassilios P., Kristiansen, Karsten, Kouskoumvekaki, Irene
Format: Article
Language:English
Subjects:
Animal Anatomy
Animals
Cell Line
Chemistry
Chemistry and Materials Science
Chromatography
Computer aided design
Computer Applications in Chemistry
Dose-Response Relationship, Drug
Drug Discovery - methods
Fibroblasts
Histology
Hypoglycemic Agents - analysis
Hypoglycemic Agents - chemistry
Ligands
Mice
Morphology
Natural products
Oleoresins
Physical Chemistry
Pistacia - chemistry
Plant Extracts - analysis
Plant Extracts - chemistry
PPAR gamma - agonists
PPAR gamma - analysis
PPAR gamma - chemistry
Screening
Triterpenes - analysis
Triterpenes - chemistry
VAR
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Summary:In a search for more effective and safe anti-diabetic compounds, we developed a pharmacophore model based on partial agonists of PPARγ. The model was used for the virtual screening of the Chinese Natural Product Database (CNPD), a library of plant-derived natural products primarily used in folk medicine. From the resulting hits, we selected methyl oleanonate, a compound found, among others, in Pistacia lentiscus var. Chia oleoresin (Chios mastic gum). The acid of methyl oleanonate, oleanonic acid, was identified as a PPARγ agonist through bioassay-guided chromatographic fractionations of Chios mastic gum fractions, whereas some other sub-fractions exhibited also biological activity towards PPARγ. The results from the present work are two-fold: on the one hand we demonstrate that the pharmacophore model we developed is able to select novel ligand scaffolds that act as PPARγ agonists; while at the same time it manifests that natural products are highly relevant for use in virtual screening-based drug discovery.
ISSN:0920-654X
1573-4951
DOI:10.1007/s10822-010-9398-5